Automated parasite analysis system

ABSTRACT

A parasite analysis system includes a pressure vessel configured to store a biological sample, an imaging cell connected to the pressure vessel, and a waste depository connected to the imaging cell. An input valve controls whether biological sample can flow from the pressure vessel into the imaging cell and an output valve controls whether biological sample can flow from the imaging cell into the waste depository. The parasite analysis system also includes a camera that captures a chronological set of images of a portion of the biological sample in the imaging cell and an image analysis system that analyzes the chronological set of images to generate an estimate of a number of parasites in the portion of the biological sample. Estimates for multiple portions of the biological sample may be generated and sampling techniques used to estimate the number of parasites in the entire biological sample.

TECHNICAL FIELD

The disclosure generally relates to the field of biological sampleanalysis, and in particular to a system for automatically counting thenumber of parasites in a sample.

BACKGROUND

Many parasites live within or on animals. One class of parasites liveswithin the digestive tracts of animals, such as sheep. These parasitescan cause a wide range of problems, including disease and even death ifleft untreated. In agricultural settings, parasitic infections can havea serious impact on productivity of livestock. Furthermore, in someinstances, such infections may have detrimental effects on humans whocome into contact with the livestock.

To lessen these effects, there is on-going work to develop newmedications and other treatments to reduce or eliminate parasiticinfections. When a potential new treatment is developed, testing isperformed to evaluate its efficacy. Existing testing methods involveextracting samples from animals' digestive tracts with various treatmentand infection statuses (e.g., a group that is infected and untreated, agroup that is infected and been treated, and an uninfected controlgroup). A human then manually counts the number of parasites present ineach sample to compare the groups and determine efficacy of thetreatment. This is both unpleasant work for the human and prone toerrors.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments have advantages and features which will bemore readily apparent from the detailed description, the appendedclaims, and the accompanying figures (or drawings). A brief introductionof the figures is below.

Figure (FIG. 1 illustrates an example automated parasite analysissystem, according to one embodiment.

FIG. 2 illustrates an image analysis system configured for use in theautomated parasite analysis system of FIG. 1, according to oneembodiment.

FIG. 3 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller), according to one embodiment.

FIG. 4 is a flowchart illustrating an example method for automaticallycounting the number of parasites in a sample, according to oneembodiment.

FIG. 5 is a flowchart illustrating an example method for analyzingimages of a sample to determine the number of parasites in the sample,according to one embodiment.

FIG. 6 illustrates an alternate configuration of an example parasiteanalysis system, according to one embodiment.

DETAILED DESCRIPTION

The Figures (FIGS.) and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the disclosed system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

Configuration Overview

As noted previously, counting parasites in a biological sample manuallymay be an unpleasant task and is prone to errors. The exampleembodiments disclosed may solve these and other problems by automating(or semi-automating) this task. A physical system controls the flow of abiological sample (e.g., taken from an animal's digestive tract) throughan imaging cell. The physical system may be enclosed such that, once thesample has been added, human operators are not exposed to the sample.Portions of the sample are isolated in the imaging cell and a cameracaptures images of the isolated portions. Computer vision is used tocount the number of parasites present in the isolated portions of thesample. Sampling techniques may then be used to estimate the totalnumber of parasites in the sample.

Disclosed by way of example is a configuration that may include aparasite analysis system (and/or corresponding method) for counting thenumber of parasites in a biological sample. In one embodiment, thesystem includes a pressure vessel configured to store a biologicalsample, an imaging cell connected to the pressure vessel, and a wastedepository connected to the imaging cell. An input valve controlswhether biological sample can flow from the pressure vessel into theimaging cell and an output valve controls whether biological sample canflow from the imaging cell into the waste depository. The parasiteanalysis system also includes a camera that captures a chronological setof images of a portion of the biological sample in the imaging cell andan image analysis system that analyzes the chronological set of imagesto generate an estimate of a number of parasites in the portion of thebiological sample. Estimates for multiple portions of the biologicalsample may be generated and sampling techniques used to estimate thenumber of parasites in the entire biological sample.

Example Automated Parasite Counting System

FIG. 1 illustrates an example embodiment of an automated parasiteanalysis system 100. In the embodiment shown in FIG. 1, the automatedparasite analysis system 100 includes a pressure vessel 110, an imagingcell 120, a waste depository 130, a camera 160, and an image analysissystem 170. Flow of a biological sample to and from the imaging cell 120is controlled by an input valve 115 a and an output valve 115 b, whichare controlled by a controller 150 using a relay 140. In otherembodiments, the networked computing environment 100 may includedifferent and/or additional elements. In addition, the functions may bedistributed among the elements in a different manner than described. Forexample, although the controller 150 and image analysis system 170 areshown as separate entities, in one embodiment, the functionality of eachis provided by a single computing device.

The pressure vessel 110 holds a biological sample for which a count ofthe number of parasites is desired. The pressure vessel 110 includes oris connected to a pump (e.g., an air pump) to maintain an internalpressure greater than the ambient pressure. The content of the pressurevessel 110 may be continuously or periodically mixed (e.g., using amagnetic stirrer) to prevent the sample from separating (e.g., withorganic material sinking to the bottom of the vessel) making theportions that flow into the imaging cell 120 unrepresentative. Forclarity and convenience, the automated parasite analysis system 100 isdescribed in various places below with reference to an exampleembodiment where the biological sample is the extracted content of asheep's stomach. However, one of skill in the art will recognize thatthe automated parasite analysis system 100 may be used with otherbiological samples.

In the embodiment shown in FIG. 1, the pressure vessel 110 is connectedto an imaging cell 120, which is in turn connected to a waste depository130. The imaging cell 120 may be a transparent cell aligned with acamera 160 for capturing images of a portion of the biological sample(e.g., some or all of the sample currently contained within the cell).The waste depository 130 may be a tank, vessel, or other mechanism fordisposing of the biological sample. In one embodiment, the wastedepository 130 is a sealed container. Thus, the automated parasiteanalysis system 100 may keep the biological sample enclosed duringtesting, preventing exposure of human operators from hazards and/orodors associated with the biological sample.

Flow of the biological sample to the waste depository 130 through theimaging cell 120 is controlled by an input valve 115 a (between thepressure vessel 110 and the imaging cell) and an output valve 115 b(between the imaging cell and the waste depository). When the valves 115are open, the stomach content is pushed through the imaging cell 120 andinto the waste depository 130 under the pressure of the pressurizedvessel 110. After sufficient time has passed for the content of theimaging cell to be completely (or substantially) replaced (e.g., 100milliseconds to 1 second), the valves 115 may be closed. The content ofthe imaging cell may be considered to be substantially replaced if apredefined percentage (e.g., 70%, 80%, 90%, 95%, etc.) by volume of thecontent has changed. In some cases, the valves 115 remain open until itis statistically likely that more than the volume of the imaging cell120 has been replaced (e.g., 140%, 180%, 200%, etc. of the image cellvolume) to ensure none of the sample is imaged twice In otherembodiments, multiple imaging cells 120 are connected to the pressurevessel 110 in series and/or parallel. This can reduce the amount of timerequired to analyze the biological sample.

In one embodiment, the valves 115 are solenoid valves that arecontrolled by a relay 140. The solenoid valves 115 may be normally openvalves (meaning they are open unless the relay 140 applies a current tothe solenoids to close the valves) or normally closed valves (meaningthey are closed unless the relay applies a current to open the valve).Whether the relay 140 applies a current to the valves 115 is determinedby a controller 150. The controller 150 may be a micro-controller,desktop computer, or other such electronic device. In other embodiments,different types of valves 115 and/or control mechanisms may be used.

Under various circumstances, the pressure in the pressure vessel 110 maychange. For example, when the valves 115 are open and the biologicalsample is flowing through the imaging cell, the pressure in the pressurevessel may drop. In one embodiment, the pressure vessel 110 includes oneor more pressure sensors that periodically or continuously send pressurereadings to the controller. The controller 150 provides pressure controldata (e.g., instructions to a pump to pump air into the pressure vessel)to maintain the pressure within the pressure vessel 110. In other words,the pressure vessel sensors and controllers 150 operate as a feedbackloop to maintain the pressure in the pressure vessel 110 at a desiredlevel. Alternatively, the pressure vessel 110 may include a separatesystem (e.g., an integrated microcontroller) for maintaining the desiredpressure.

The camera 160 captures images of a portion of the biological samplecurrently within the imaging cell 120. The camera 160 can capture imagesof the entire imaging cell 120 or a portion of the cell (e.g., a centralimaging portion). The camera may include optical components to magnifythe portion of biological sample being imaged or be connected to amicroscope or other magnification device so that parasites may beresolved in the captured images. In one embodiment, after the valves 115have been closed, the system 100 waits for a predetermined period (e.g.,two to three seconds) for the portion of the biological sample withinthe imaging cell 120 to settle. The camera 160 then captures achronologically ordered set of images (e.g., five to ten seconds ofvideo) that is sent to the image analysis system 170 for furtherprocessing. The images may be streamed to the image analysis system 170during capture or sent in bulk at specified times, such as on completionof capture of the set, on instruction by a user of the image analysissystem 170, and/or on a periodic schedule (e.g., once a day).

Once the camera 160 has captured the images, the valves 115 may bereopened. The pressure from the pressure vessel 110 pushes the portionof the biological sample that was imaged into the waste depository 130and replaces it in the imaging cell 120 with a new, yet to be imagedportion from the pressure vessel 110. The camera 160 may then captureimages of the new portion and the process can be repeated until an endcondition is met. In one embodiment, the end condition is that all (orsubstantially all) of the biological sample has been moved from thepressure vessel 110 to the waste depository 130. In other embodiment,the end condition is that a specified number of portions of thebiological sample have been imaged by the camera 16. In otherembodiments, other end conditions may be used.

The image analysis system 170 analyzes the images of a portion of thebiological sample captured by the camera 160 to determine the number ofparasites present in the imaged portion. In various embodiments, theimage analysis system 170 identifies moving objects by analyzingdifferences between images in a chronologically ordered set of imagesand identifies regions that are candidates for including parasites. Inthe case where the biological sample is the extracted contents of asheep's stomach, the parasites may be worms that have a known range ofsizes. Thus, candidate regions can be specified based on an expectedsize range for worms. The image analysis system 170 applies a classifierto distinguish between candidate regions that that contain parasites andthose that do not (e.g., those that contain grass or other partiallydigested food). The classifier may be heuristic, machine-learned, or usea combination of both approaches. Various example embodiments of theanalysis performed by the image analysis system 170 are described ingreater detail below, with reference to FIGS. 2 and 5.

In some embodiments, the biological sample is diluted with anappropriate solvent (e.g., water) either in the pressure vessel 110 orbefore it is added to the pressure vessel. Dilution of the biologicalsample may reduce the likelihood of the valves 115 becoming clogged. Itmay also reduce the likelihood that parasites are close enough togetherthat they overlap or are otherwise in close enough proximity in themages captured by the camera 160 that the individual parasites aredifficult to identify. For example, if the parasite analysis system 100uses candidate regions that are approximately the size of a parasite,the sample may be diluted such that the probability of two parasitesbeing within the same region being sufficiently small that this scenariocan be discounted without significantly impacting the accuracy of theresults. On the other hand, dilution increases the volume of the samplethat is imaged. Therefore, the amount of dilution used may be selectedto balance the advantages of less clogging and less likelihood of animage region containing two (or more) parasites with the increased timerequired for imaging.

Some of the components of the parasite analysis system 100 maycommunicate via a network (not shown). For example, the camera 160 mighttransmit captured images to the image analysis system 170 over thenetwork. The network can include any combination of local area and/orwide area networks, using both wired and/or wireless communicationsystems. In one embodiment, the network uses standard communicationstechnologies and/or protocols. The network can include communicationlinks using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 3G, 4G, 5G, code divisionmultiple access (CDMA), digital subscriber line (DSL), etc. Examples ofnetworking protocols used for communicating via the network includemultiprotocol label switching (MPLS), transmission controlprotocol/Internet protocol (TCP/IP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP), and file transfer protocol(FTP). Data exchanged over the network may be represented using anysuitable format, such as hypertext markup language (HTML), extensiblemarkup language (XML). In particular, chronological sets of imagescaptured by the camera 160 may be transferred in suitable formats, suchas Matroska Multimedia Container (MKV), MPEG-4, or corresponding vectorrepresentations (e.g., NumPy or Comma-Separated Variable (CSV) files).In some embodiments, all or some of the communication links of thenetwork 170 may be encrypted using any suitable technique or techniques.

FIG. 2 shows one embodiment of an image analysis system 170 thatanalyzes images of a sample to determine the number of parasites in thesample. In the embodiment shown in FIG. 2, the image analysis system 170includes a foreground extraction module 210, a candidate detectionmodule 220, a candidate classification module 230, a post-processingmodule 240, and an image store 250. In other embodiments, the imageanalysis system 170 may include different and/or additional elements. Inaddition, the functions may be distributed among the elements in adifferent manner than described.

The foreground extraction module 210 receives a set of chronologicallyordered images from a camera 160 (e.g., video of the imaging cell 120)and identifies foreground pixels. The foreground pixels are those pixelsof the images that change (or change more than a threshold amount)relative to substantially static background parts of the image. Forexample, an edge of the imaging cell 120 will be in the same location inevery image (assuming the camera 160) has not removed and is thus abackground pixel. In contrast, a parasitic worm swimming across theimaging cell 120 will appear at different positions in each image. Inone embodiment, the foreground extraction module 210 uses robustprincipal component analysis (RPCA) to extract the foreground pixelsfrom the images. The designation of a pixel as a foreground pixel may bebinary (e.g., a label indicating a pixel is either foreground orbackground) or include a foreground pixel score (e.g., a probabilitythat the pixel corresponds to a foreground feature). In otherembodiments, other background subtraction and/or feature detectionalgorithms may be used (e.g., neural networks).

The candidate detection module 220 identifies patches of the images thatare likely to include a parasite. The set of patches identified in animage may be non-overlapping or may be restricted to overlapping by nomore than a threshold amount (e.g., 20%, etc.). In various embodiments,the approximate size of parasites is known. For example, where thebiological sample is the extracted content of a sheep's stomach that isbeing tested for a particular parasitic worm, the typical size range ofthat worm is known. Based on the magnification level of the images andthe distance between the camera 160 and the imaging cell 120, thecandidate detection module 220 can determine an expected range of sizesfor parasites in the images. Alternatively, this range of sizes can bepre-determined and provided as a parameter.

In one embodiment, the candidate detection module 220 defines arectangular window that is large enough encompass the largest expectedsize of parasite (or any parasite within a given percentile of theexpected size range, such as 99%). The window is placed at each possiblepatch of that size in an image and the candidate detection module 220calculates a foreground pixel score for the patch. The foreground pixelscore may be calculated by counting the number of foreground pixels inthe window at that location. In other words, the number of pixels thatcorrespond to something that is moving from one image to the next. Thewindow with the highest score is found. If this score exceeds athreshold, the corresponding patch is flagged as a candidate patch forincluding a parasite. The process is repeated for window locations thatdo not overlap with candidate patches (or overlap by less than athreshold amount) until an end condition is met (e.g., no more candidatepatches may be identified for which the foreground pixel score exceedsthe threshold). Alternatively, overlap between candidate patches may beavoided or reduced by setting all of the pixels within a candidate patchto be background pixels (e.g., given a foreground feature score ofzero). Other forms of non-maxima suppression may be used. Thus, for eachimage in the set, the candidate detection module 220 flags patches thatare candidates for including a parasite.

In another embodiment, the contribution of a given pixel to theforeground pixel score is weighted based on the given pixel's positionin the window. For example, the candidate detection module 220 can applya two-dimensional function centered at the center of the window tocalculate the weight for a pixel such that pixels near the center of thewindow contribute more than those around the edges. The standarddeviation for the Gaussian function may be set to control the extent towhich distance from the center of the window impacts weighting.Typically, a relatively large standard deviation is used so that pixelsin the center of the window do not dominate the determination, but anystandard deviation may be used. In other embodiments, other weightingfunctions and/or other techniques for identifying candidate patches maybe used.

Regardless of the specific approach used to identify the candidatepatches, in one embodiment, the candidate patches are filtered based onwhether preceding and following images in the set also have candidatepatches at similar locations. For example, based on an expected range ofspeeds for parasites and the time between images, the candidatedetection module 220 may identify candidate patches in other images thatmay correspond to the same parasite as a candidate patch (assuming oneis present). If the images preceding and/or following images do not havecandidate patches that are likely to correspond to the candidate patchin the current image, the candidate detection module 220 may determinethat it is an artifact and remove it as a candidate. Using this or asimilar approach across multiple images, the candidate detection module220 may track the movement of an object from image to image (e.g., speedand direction). This movement may be used to aid in determining whetherthe candidate patches correspond to parasites or some other object. Notethat, although this functionality is described in the context of thecandidate detection module 220, the same or similar functionality mayadditionally or alternatively be provided by the post-processing module240.

The candidate classification module 230 applies a classifier to thecandidate patches to determine whether they include a parasite. Invarious embodiments, the classifier is based on variance in histogramsof gradients determined from the candidate patches. The candidateclassification module 230 determines the gradient at each pixel in thecandidate patch. The gradients have an orientation and a magnitude. Theorientation may indicate the direction from the current pixel in whichpixel intensity changes the most rapidly (e.g., in degrees) and themagnitude indicates the extent of that intensity change. The orientationmay be signed (e.g., ranging from 0 to 360 degrees) or unsigned (e.g.,ranging from 0 to 180 degrees).

The candidate classification module 230 defines a set of bins, eachcorresponding to a range of orientations. For example, if unsignedorientations are used, there might be nine bins that are each twentydegrees wide (e.g., 0 to 20 degrees, 20 to 40 degrees, 40 to 60 degrees,etc.). Various examples are given below based on this distribution ofbins, but other distributions may be used. The bins need not be of equalsize and any number of bins may be used.

The candidate classification module 230 calculates a histogram ofgradients for the patch by determining the contribution of the gradientof each pixel to one or more bins. In one embodiment, each pixelgradient is assigned to the bin that encompasses its orientation and themagnitude of the pixel gradient is added to a cumulative total for thatbin. For example, a gradient with magnitude sixteen at an orientation offifteen degrees may add sixteen to the “0 to 20 degrees” bin. In anotherembodiment, the contribution of a pixel gradient is divided between thebins based on proximity of the pixel gradient orientation to the centerof the bins. For example, in the previous case of the gradient ofmagnitude sixteen with an orientation of fifteen degrees, the gradientorientation is five degrees from the center of the “0 to 20 degrees” binand fifteen degrees from the “20 to 40 degrees” bin. Thus, the gradientmay add twelve to the “0 to 20 degrees” bin and four to the “20 to 40degrees” bin. In other words, the contribution of the gradient to eachbin is inversely proportional to the distance of the orientation fromthe center of each bin. In other embodiments, other ways of weightingthe contribution of gradients to two or more bins may be used.

Regardless of the specific details of how the gradient of histograms iscalculated for a candidate patch, the candidate classification module230 also calculates a gradient of histograms for the same portion of asubset of the other images in the set of images (e.g., the image inwhich the candidate patch was identified and the nine images immediatelyfollowing it in the set, a set of nine consecutive images centeredaround the image in which the candidate patch was identified, or thelike). Large differences in the histograms for images that were capturedclose together in time (e.g., consecutive images) are indicative of alive parasitic worm being present in the patch. This is because suchworms swim in a snake like manner, whereas dead worms, partiallydigested food, and other non-parasite objects will tend to drift inapproximately straight lines. However, a relatively large object, suchas a blade of grass, moving across the frame from image to image maystill generate some gradient changes as the object covers and uncoversunderlying background.

In one embodiment, the candidate classification module 230 calculates adifference metric between each pair of consecutive images in the set andclassifies the patch as including parasite if the average of thedifference metrics exceeds a threshold. In one example, the differencemetric is calculated by finding the standard deviation of each histogrambin over time and summing the standard deviations. The threshold usedmay be optimized through cross validation as it may depend on otherparameters, such as the size of the candidate patches used, the lengthof time for which data is collected, and the like. In anotherembodiment, the motion vector field is analyzed to distinguish betweenobjects that exhibit snake-like motion (e.g., parasites) and objectsthat move largely in one direction (e.g., floating debris).

In another set of embodiments, the classifier is a machine-learnedclassifier, such as a neural network, a gradient boosted decision tree,a support vector machine, or the like. In these embodiments, a set oftraining data is collected and the training data is labelled by humanoperators as either including or not including a parasite. In one suchembodiment, the human operators may identify the set of pixels thatcorrespond to the worm (e.g., by tracing its outline on the image usinga touchscreen). The training data may be, for example, a set of patchesthat either do or do not contain a parasite. The classifier is thentrained (e.g., using backpropogation) to correctly sort the trainingdata into those that are labelled as containing a parasite and thosethat are not. The training of the classifier may also be validated byapplying it to a set of validation data that has also beenhuman-labelled but which was not used for the training. Once trained(and validated, if validation is used), the classifier may then beapplied in place of (or in conjunction with) a gradient of histogramsapproach to classify candidate patches as either including or notincluding a parasite.

Regardless of the specific approach used to classify the candidatepatches, the end result is the post-processing module 240 produces acount of the number of parasites in each image based on the candidatepatch classifications. In one embodiment, the count for the number ofparasites in an image is the number of candidate patches classified ascontaining a parasite. In another embodiment, each classificationincludes a level of certainty (e.g. a probability that theclassification is correct) and the count is determined by the levels ofcertainty. For example, the post-processing module 240 may sum theprobability for each candidate patch or perform some other weightedcombination based on the levels of certainty. The count for a givenimage may also be influenced by the location of candidate patches inother images in the set. For example, as noted previously, a candidatepatch is more likely to genuinely include a parasite if there areparasites at similar locations in the preceding and following images.Thus, the contribution of a candidate patch to the count may be weightedbased on proximity to candidate patches in other images.

The post-processing module 240 takes the parasite counts for the set ofimages and estimates the total number of parasites in the biologicalsample. In one embodiment, the total number of parasites is estimated ina two-stage process: (1) the number of parasites in each portion of thebiological sample imaged is estimated; and (2) the total number ofparasites in the biological sample is estimated based on the estimatedparasite counts for the imaged portions.

The post-processing module 240 estimates the number of parasites in aparticular portion of the biological sample based on the individualparasite counts for images in the set of images of that portion capturedby the camera 160. In one embodiment, to estimate the number ofparasites in the portion, the post-processing module 240 calculates themedian of the individual parasite counts determined from the images inthe set. This may be a reasonable measure of the number of parasites inthat portion assuming the number of false positives and false negativesare approximately equal. In other embodiments, other methods fordetermining the number of parasites in a portion of the biologicalsample may be used, such as taking the maximum, mean, or mode of theindividual counts. A total number of detected parasites may then becalculated by summing the estimates for each imaged portion.

As described previously, the automated parasite analysis system 100captures sets of images of multiple portions of biological sample. Thepost processing module 240 may estimate the total parasite count for thebiological sample by assuming the imaged portions are representative ofthe whole (e.g., that the imaged portions are a sufficient fraction ofthe whole to be a statistically valid sample). For example, theautomated parasite counting system 100 may image approximately 10% ofthe total volume of the biological sample. In one embodiment, the postprocessing module 240 determines the total volume of the biologicalsample and the total volume of the imaged portions. The total volume ofthe biological sample may be determined by measuring it in the pressurevessel 110 before any imaging is performed. Alternatively, the totalvolume may be measured elsewhere, such as in the waste depository 130.

The total volume of the imaged portions may be determined by multiplyingthe volume of the imaging cell 120 by the number of portions imaged. Thepost processing module 240 may then estimate the total number ofparasites in the biological sample by multiplying the total number ofdetected parasites by the ratio of the total volume of the biologicalsample and the total volume of the imaged portions. Additionally oralternatively, other statistical sampling methods may be used toestimate the total number of parasites from the number of parasitesdetected.

The image store 250 includes one or more computer-readable mediaconfigured to store the images captured by the camera 160. The imagestore 250 may also store metadata in association with the images, suchas date and time of capture, the source of the biological sample, thetype of the biological sample, the type of the parasite being counted,an identifier of a drug trial for which the biological sample is beinganalyzed, the parasite counts for the individual images determined bythe candidate classification module 230, the parasite counts determinedfor the sets of images determined by the post-processing module, and thelike. Although the image store 250 is shown as a single entity withinthe image analysis system 170, in some embodiments, it may be locatedremotely at one or more other computing devices (e.g., in a distributeddatabase).

Example Machine Architecture

FIG. 3 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller). Specifically, FIG. 3 shows adiagrammatic representation of a machine in the example form of acomputer system 300. The computer system 300 can be used to executeinstructions 324 (e.g., program code or software) for causing themachine to perform any one or more of the methodologies (or processes)described herein, including those associated, and described, with thecomponents (or modules) of a controller 150 and/or image analysis system170. In alternative embodiments, the machine operates as a standalonedevice or a connected (e.g., networked) device that connects to othermachines. In a networked deployment, the machine may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a smartphone, aninternet of things (IoT) appliance, a network router, switch or bridge,or any machine capable of executing instructions 324 (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute instructions 324 to perform any one or more of themethodologies discussed herein.

The example computer system 300 includes one or more processing units(generally one or more processors 302). The processor 302 is, forexample, a central processing unit (CPU), a graphics processing unit(GPU), a digital signal processor (DSP), a controller, a state machine,one or more application specific integrated circuits (ASICs), one ormore radio-frequency integrated circuits (RFICs), or any combination ofthese. Any reference herein to a processor 302 may refer to a singleprocessor or multiple processors. The computer system 300 also includesa main memory 304. The computer system may include a storage unit 316.The processor 302, memory 304, and the storage unit 316 communicate viaa bus 308.

In addition, the computer system 300 can include a static memory 306, adisplay driver 310 (e.g., to drive a plasma display panel (PDP), aliquid crystal display (LCD), or a projector). The computer system 300may also include alphanumeric input device 312 (e.g., a keyboard), acursor control device 314 (e.g., a mouse, a trackball, a joystick, amotion sensor, or other pointing instrument), a signal generation device318 (e.g., a speaker), and a network interface device 320, which alsoare configured to communicate via the bus 308.

The storage unit 316 includes a machine-readable medium 322 on which isstored instructions 324 (e.g., software) embodying any one or more ofthe methodologies or functions described herein. The instructions 324may also reside, completely or at least partially, within the mainmemory 304 or within the processor 302 (e.g., within a processor's cachememory) during execution thereof by the computer system 300, the mainmemory 304 and the processor 302 also constituting machine-readablemedia. The instructions 324 may be transmitted or received over anetwork 370 via the network interface device 320.

While machine-readable medium 322 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 324. The term “machine-readable medium” shall also betaken to include any medium that is capable of storing instructions 324for execution by the machine and that cause the machine to perform anyone or more of the methodologies disclosed herein. The term“machine-readable medium” includes, but not be limited to, datarepositories in the form of solid-state memories, optical media, andmagnetic media.

Example Methods for Automatically Counting Parasites

FIG. 4 illustrates one embodiment of an example method 400 forautomatically counting the number of parasites in a sample. The steps ofFIG. 4 are illustrated from the perspective of various components of theautomated parasite analysis system 100 performing the method 400.However, some or all of the steps may be performed by other entitiesand/or components. In addition, some embodiments may perform the stepsin parallel, perform the steps in different orders, or perform differentsteps. For example, although FIG. 4 shows image analysis being performedafter all of the images have been captured, in some embodiments, one setof images may be analyzed while additional images are being captured.

In the embodiment shown in FIG. 4, the method 400 begins with abiological sample being placed 410 in a pressure vessel 110. This may bedone where the biological sample is collected with the pressure vessel110 transported and then connected to the rest of the automated parasiteanalysis system 100. For example, the biological sample might becollected at a farm and the transported to a laboratory for testing.Alternatively, the pressure vessel may be permanently orsemi-permanently installed with the biological sample being placed 410within it on-site.

The controller 150 opens 420 the valves 115 to enable a portion of thebiological sample to flow into the imaging cell 120. If this is thefirst portion of the biological sample to be imaged, the imaging cell120 may be initially empty or may contain existing fluid (e.g., aportion of a different biological sample, cleaning fluid, water, or thelike). If this is not the first portion of the biological sample to beimaged, the imaging cell 120 may contain a previously imaged portion ofthe biological sample. In either case, the valves 115 remain open forany existing content to be pushed out of the imaging cell 120 into thewaste depository 130 to be replaced by the incoming portion of thebiological sample. In one embodiment, the valves 115 remain open for aperiod in the range from one hundred milliseconds to one second. Theprecise time may be selected to balance certainty that substantially allof the content of the imaging cell 120 has been replaced with minimizingthe amount of the biological sample that passes through the wastedepository 130 without being imaged. Once the valves 115 have been openfor the specified time, the controller 150 closes 430 the valves,stopping the flow of biological sample.

The automated parasite analysis system 100 waits 440 for the portion ofthe biological sample in the imaging cell 120 to settle. As the portionhas been forced into the imaging cell 120 under pressure, when thevalves 115 are initially closed 430, there may be significant turbulencein the portion. The resulting motion of objects within the biologicalsample may make distinguishing between parasites and non-living entitiesdifficult. In one embodiment, the waiting period is between two andthree seconds, but other periods may be used.

Once the waiting period has passed, the camera 160 captures 450 achronological set of images (e.g., five to ten seconds of video) of theportion of the sample in the imaging cell 120. The images may be equallyspaced in time (e.g., video captured at 24 or 30 frames per second) orunequally spaced with the time of capture for each image being stored asmetadata in association with the image. For example, the camera 160might automatically adjust a shutter speed to normalize an averagebrightness of the captured images leading to changes in the time betweenimages if the background illumination level changes significantly.Regardless of the specific timing of the capture of the images, thecamera 160 sends the set of images to an image analysis system 170 to bestored (e.g., in an image store 250).

Once the set of images has been captured 450, the automated parasiteanalysis system 100 determines whether there is more of the biologicalsample to image. If so, the automated parasite analysis system 100repeats the steps of opening 420 the valves 115, closing 430 the valves,waiting 440 for the new portion of the biological sample in the imagingcell 120 to settle, and capturing 450 a chronological set of images ofthe new portion of the biological sample.

The image analysis system 170 analyzes 460 the chronological sets ofimages to estimate the total number of parasites present in thebiological sample. As described previously, various approaches may beused to estimate the number of parasites present in the imaged portionsof the biological sample, including a histogram of gradients approachand machine learning. Various sampling techniques can then be used toestimate the total number of parasites present in the entire biologicalsample based on the estimated counts for the imaged portions. Exampleembodiments of methods for analyzing a set of images are described ingreater detail below, with reference to FIG. 5.

In one embodiment, the automated parasite analysis system 100 analyzesbiological samples from different stages of a trial of an anti-parasitetreatment. For example, biological samples might be collected fromgroups of sheep at different stages in the treatment process, such asimmediately before treatment begins, the day after treatment begins, tendays after treatment begins, the day treatment ends, a month aftertreatment ends, or the like, as well as from one or more control groups(e.g., a group that is infected and untreated and/or a group that is notinfected). Thus, the efficacy over time of the treatment can bedetermined by comparing the parasite counts determined for eachbiological sample.

FIG. 5 illustrates one embodiment of an example method 500 for images ofa sample to determine the number of parasites in the sample (e.g., asstep 460 in the method 400 illustrated by FIG. 4). The steps of FIG. 5are illustrated from the perspective of the image analysis system 170performing the method 500. However, some or all of the steps may beperformed by other entities and/or components. In addition, someembodiments may perform the steps in parallel, perform the steps indifferent orders, or perform different steps.

In the embodiment shown in FIG. 5, the method 500 begins with imageanalysis system 170 retrieving 510 a chronological set of imagesdepicting a portion of a biological sample (e.g., a set captured by thecamera 160). The images may be retrieved directly from the camera 160 orfrom a data store (e.g., image store 250). The images in the set depictthe portion of the biological sample at different times. The timingand/or order of the images may be indicated by metadata that isretrieved along with the images.

The foreground extraction module 210 identifies 520 foreground pixels inthe images. Based on the foreground extraction module results, thecandidate detection module 220 identifies 530 candidate regions(patches) in the images that may correspond to parasites. As describedpreviously, in one embodiment, RPCA is applied to the set of images toidentify 520 pixels that significantly change across the set of imagesas foreground pixels and a sliding window is used to identify 530 thosepatches in an image for which a foreground pixel score for the patchexceeds a threshold. As described previously, a non-maxima suppressiontechnique may be applied to prevent a single parasite resulting inmultiple candidate patches being identified with high confidence levels(e.g., by changing foreground pixels within identified patches tobackground pixels, not allowing candidate patches to overlap by morethan a threshold amount, etc.). The foreground pixel score may be acount of the number of foreground pixels in the patch or a weighted sumof foreground pixels (e.g., where the contribution of a given foregroundpixel is subject to weighting determined by a two-dimensional Gaussianfunction centered at the center of the patch). The candidateclassification module 230 classifies 540 the candidate regions toidentify those that likely correspond to parasites. In one embodiment,the histogram of gradients approach described above, with reference toFIG. 2, is used to classify 540 the candidate patches. In otherembodiments, other classification techniques may be used, such asapplying a machine-learning model. Regardless of the specificclassification method used, the result is that the candidateclassification module 230 identifies a set of patches that are likely tocorrespond to parasites. In one embodiment, the candidate classificationmodule 230 outputs a set of classified patches (e.g., locations andconfidence levels that each patch depicts a parasite) for each image inthe set. An image may have zero candidate patches.

The post-processing module 240 determines 550 a count for the number ofparasites in the portion based on the parasite counts for the individualimages in the set. In one embodiment, post-processing module 240calculates the median of the parasite counts for the individual images.Other forms of averaging may also be used, such as the mean (with orwithout rounding), mode, etc.

If there is one or more sets of images for the current biological samplethat are still to be analyzed, the image analysis system 170 repeats thesteps of retrieving 510 a set of images of a portion of a biologicalsample, identifying 520 foreground pixels in the images, identifying 530candidate regions of the images, classifying 540 the candidate regions,and determining 550 a count for the number parasites in the portion ofthe biological sample.

Based on the determined 550 parasite counts for the sets of images ofportions of the biological sample, the post-processing module 240determines 560 an estimate for the total number of parasites in thebiological sample. As described previously, any appropriate samplingtechnique may be used to convert the number of parasites detected in theportions of the sample that were imaged into an estimate of the numberof parasites in the whole sample.

Alternate Parasite Analysis System Configuration

FIG. 6 illustrates an example embodiment of an alternate configurationfor a parasite analysis system 600. In contrast to the embodiment shownin FIG. 1, the parasite analysis system 600 stores unanalyzed samplematerial in a material reservoir 610. The material reservoir 610 can beany container or vessel suitable for holding the biological sample to beanalyzed. Unlike the pressure vessel 110 in FIG. 1, the materialreservoir 610 need not be pressurized (although it may be).

A three-way valve 615 controls flow of the biological sample from thematerial reservoir 610 to a piston/syringe 612 and from thepiston/syringe 612 to the imaging cell 120. At the beginning of a cycle,the controller 150 sets the three-way valve to open the connectionbetween the material reservoir 610 and the piston/syringe 612 (e.g.,using the relay 140). The controller 150 sends a signal to thepiston/syringe 612 causing it to suck a portion of the biological samplefrom the material reservoir 610 into the piston/syringe 612. Thecontroller 150 then sets the three-way valve 615 to open the connectionbetween the piston/syringe 612 and the imaging cell 120 and sends asignal to the piston/syringe 612 causing it to push the portion of thebiological sample into the imaging cell 120 for imaging by the camera160. The incoming portion of the biological sample may force anyexisting content of the imaging cell 120 into the waste depository 130.This cycle may be repeated until the desired amount of the biologicalsample has been imaged.

Note that in the embodiment shown in FIG. 6, the automated parasiteanalysis system 600 does not include a valve between the imaging cell120 and the waste depository 130. However, in some embodiments, such avalve may be included (e.g., an output valve 115 b). The embodimentshown in FIG. 6 may be advantageous in certain cases for variousreasons. For example, the use of less valves may result in the system600 being less prone to clogging and thus require less maintenance. Asanother example, by replacing the pressure vessel 110 with a materialreservoir 610, the system 600 may be cheaper and/or more convenient tooperate.

ADDITIONAL CONSIDERATIONS

The disclosed approaches enable the number of parasites in a biologicalsample to be determined automatically (or semi-automatically). Theseapproaches may have various advantages, including eliminating human biasfrom the counting process, saving operator time, and protectingoperators from the hazards and/or odors resulting from exposure to thebiological sample. Furthermore, where the images captured by theautomated parasite analysis system 100 are stored, the parasite countsmay be verified by further analysis, which may not be possible insystems where the parasites are counted by a human operator examiningthe biological sample.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms, for example, as illustrated inFIGS. 1 and 2. Modules may constitute either software modules (e.g.,code embodied on a machine-readable medium) or hardware modules. Ahardware module is tangible unit capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors, e.g.,processor 302, that are temporarily configured (e.g., by software) orpermanently configured to perform the relevant operations. Whethertemporarily or permanently configured, such processors may constituteprocessor-implemented modules that operate to perform one or moreoperations or functions. The modules referred to herein may, in someexample embodiments, comprise processor-implemented modules.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition X or Y is satisfied by any one of the following: X is true(or present) and Y is false (or not present), X is false (or notpresent) and Y is true (or present), and both X and Y are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process for proving that a target system 110 was configuredas intended, according to an approved recipe 131, through the disclosedprinciples herein. Thus, while particular embodiments and applicationshave been illustrated and described, it is to be understood that thedisclosed embodiments are not limited to the precise construction andcomponents disclosed herein. Various modifications, changes andvariations, which will be apparent to those skilled in the art, may bemade in the arrangement, operation and details of the method andapparatus disclosed herein without departing from the spirit and scopedefined in the appended claims.

What is claimed is:
 1. A parasite analysis system comprising: a pressurevessel configured to store a biological sample; an imaging cellcontrollably connected to the pressure vessel, wherein an input valvecontrols whether biological sample can flow from the pressure vesselinto the imaging cell; a waste depository controllably connected to theimaging cell, wherein an output valve controls whether biological samplecan flow from the imaging cell into the waste depository; a cameraconfigured to capture a chronological set of images of a portion of thebiological sample in the imaging cell; and an image analysis systemconfigured to analyze the chronological set of images to generate anestimate of a number of parasites in the portion of the biologicalsample, wherein being configured to analyze the chronological set ofimages includes being configured to: identify patches of a predeterminedsize in at least some images of the chronological set of images ascandidate patches for depicting a parasite, the predetermined size basedon an expected size of a parasite, each candidate patch being a portionof a corresponding image of the at least some images and having alocation within the corresponding image; apply a classifier to thecandidate patches to identify a subset of the candidate patches thatdepict a parasite; and generate the estimate of the number of parasitesin the portion of the biological sample based on the locations ofcandidate patches identified as depicting a parasite.
 2. The parasiteanalysis system of claim 1, further comprising: a controller configuredto open the input valve and the output valve for sufficient time for theportion of the biological sample to flow from the pressure vessel intothe imaging cell, the portion of the biological sample substantiallyreplacing previous content of the pressure vessel.
 3. The parasiteanalysis system of claim 2, wherein the portion of the biological sampleflowing from the pressure vessel into the imaging cell pushes theprevious content of the pressure vessel into the waste depository. 4.The parasite analysis system of claim 2, wherein the controller opensand closes the input valve and the output valve simultaneously.
 5. Theparasite analysis system of claim 2, wherein the controller opens theinput valve for a predetermined period of time in a range from onehundred milliseconds to one second.
 6. The parasite analysis system ofclaim 1, wherein the chronological set of images is video recorded for aperiod in a range from five seconds to ten seconds.
 7. The parasiteanalysis system of claim 1, wherein the camera is further configured tocapture the chronological set of images in a period beginning in a rangefrom two seconds to three seconds after the input valve is closed. 8.The parasite analysis system of claim 1, wherein the image analysissystem being configured to analyze the chronological set of imagescomprises being configured to: identify foreground pixels in at leastsome of images of the chronological set of images, the foreground pixelsbeing pixels whose values change significantly between images in theset, wherein the candidate patches are identified based on foregroundpixels included in the patches.
 9. The parasite analysis system of claim8, wherein, to identify patches in at least some images of thechronological set of images as candidate patches, the image analysissystem is configured to: place a window of the predetermined size at aplurality of positions in an image; calculate, for each position of thewindow, a foreground pixel score based on a number of foreground pixelswithin the window; identify a portion of the image within the windowwhen the window is at a given position as a candidate patch responsiveto the foreground pixel score for the given position exceeding athreshold, the location of the candidate patch being the given position;and set at least some of the pixels within the window when the window isat the given position as background pixels responsive to the foregroundpixel score for the given location exceeding the threshold.
 10. Theparasite analysis system of claim 9, wherein a contribution offoreground pixels to the foreground pixel score for a position isweighted by a two dimensional Gaussian weighting function.
 11. Theparasite analysis system of claim 1, wherein, to identify candidatepatches that depict a parasite, the classifier is configured to:calculate a histogram of gradients for a candidate patch in thecorresponding image of the chronological set of images; identify acomparison portion of a second image in the chronological set of images,a location of the comparison portion in the second image correspondingto the location of the patch in the corresponding image; calculate ahistogram of gradients for the comparison portion; and determine whetherthe candidate patch depicts a parasite based on a difference between thehistogram of gradients for the candidate patch and the histogram ofgradients for the comparison portion.
 12. The parasite analysis systemof claim 1, wherein, to generate the estimate of the number of parasitesin the portion of the biological sample, the image analysis system isconfigured to: determine a parasite count for each of a plurality ofimages of the chronological set of images, the parasite count for animage based on candidate patches identified an depicting a parasite inthat image; and generate the estimate of the number of parasites bycalculating a median of the parasite counts.
 13. The parasite analysissystem of claim 1, further configured to: generate an estimate of anumber of parasites in each of a plurality of additional portions of thebiological sample; and generate an estimate for a total number ofparasites in the biological sample based on the estimates of the numberof parasites in the portion and the plurality of additional portions.14. A method for analyzing a sample, the method comprising: opening aninput valve to allow a portion of the sample to flow from a pressurevessel into an imaging cell; closing the input valve after existingcontent of the imaging cell has been substantially replaced by theportion of the sample; capturing a chronological set of images of theportion of the sample; and analyzing the chronological set of images togenerate an estimate of a number of parasites in the portion of thebiological sample, wherein the analyzing comprises: identifying patchesof a predetermined size in at least some images of the chronological setof images as candidate patches for depicting a parasite thepredetermined size based on an expected size of a parasite, eachcandidate patch being a portion of a corresponding image of the at leastsome images and having a location within the corresponding image;applying a classifier to the candidate patches to identify a subset ofthe candidate patches that depict a parasite; and generating theestimate of the number of parasites in the portion of the biologicalsample based on the locations of candidate patches identified asdepicting a parasite.
 15. The method of claim 14, wherein analyzing thechronological set of images further comprises: identifying foregroundpixels in at least some of images of the chronological set of images,the foreground pixels being pixels whose values change significantlybetween images in the set, wherein the candidate patches are identifiedbased on foreground pixels included in the patches.
 16. The method ofclaim 15, wherein identifying patches in at least some images of thechronological set of images as candidate patches comprises: placing awindow of the predetermined size at a plurality of positions in animage; calculating, for each position of the window, a foreground pixelscore based on a number of foreground pixels within the window;identifying a portion of the image within the window when the window isat a given position as a candidate patch responsive to the foregroundpixel score for the given position exceeding a threshold, the locationof the candidate patch being the given position; and setting at leastsome of the pixels within the window when the window is at the givenposition as background pixels responsive to the foreground pixel scorefor the given location exceeding the threshold.
 17. The method of claim16, wherein a contribution of foreground pixels to the foreground pixelscore for a position is weighted by a two dimensional Gaussian weightingfunction.
 18. The method of claim 14, wherein identifying candidatepatches that depict a parasite comprises: calculating a histogram ofgradients for a candidate patch in the corresponding image of thechronological set of images; identifying a comparison portion of asecond image in the chronological set of images, a location of thecomparison portion in the second image corresponding to the location ofthe patch in the corresponding image; calculating a histogram ofgradients for the comparison portion; and determining whether thecandidate patch depicts a parasite based on a difference between thehistogram of gradients for the candidate patch and the histogram ofgradients for the comparison portion.
 19. The method of claim 14,wherein generating the estimate of the number of parasites in theportion of the biological sample comprises: determining a parasite countfor each of a plurality of images of the chronological set of images,the parasite count for an image based on candidate patches identified andepicting a parasite in that image; and generating the estimate of thenumber of parasites by calculating a median of the parasite counts. 20.The method of claim 14, further comprising: generating an estimate of anumber of parasites in each of a plurality of additional portions of thebiological sample; and generating an estimate for a total number ofparasites in the biological sample based on the estimates of the numberof parasites in the portion and the plurality of additional portions.